{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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w0cuQeKQQt4/qCwAedzV+89AlKCitxDUX9MKKOuZkAVzDcBtiz5QoPPf9bizY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\nIiIb+39ib6nF1xAu9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd8b587350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_Sep/python')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import caffe\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "pp = PdfPages('multipage.pdf')\n",
    "# data = np.load('train_loss.npy')\n",
    "# np.shape(data['train_loss'])\n",
    "#-----------------------------------------------------------------------------\n",
    "# CONV_LSTM WITH RELU\n",
    "data = np.load('conv_lstm_AE_train_loss_22Sep.npy')\n",
    "print data.shape\n",
    "# print data[0:10]\n",
    "\n",
    "# print data[1]\n",
    "# data = data[1:25000]\n",
    "# # a = data[2:20000]\n",
    "# # print a.mean()\n",
    "# print data.min()\n",
    "# print data.max()\n",
    "# #\n",
    "# # print np.shape(data)\n",
    "# # print np.shape(data)\n",
    "# # print np.min(data[0:9830])\n",
    "# #----- SAVE FIGURE AS PDF FILE ---------\n",
    "# # # # plt.plot()\n",
    "# # # # ax2 = ax1.twinx()\n",
    "# # # plt.figure()\n",
    "# # # plt.clf()\n",
    "# # # plt.plot(np.arange(65000),data)\n",
    "# # # # plt.show()\n",
    "# # # plt.savefig(pp,format='pdf')\n",
    "# # # pp.savefig()\n",
    "# # # pp.close()\n",
    "# #----------------------------------------\n",
    "nS = np.shape(data)[0]\n",
    "# # print nS[0]\n",
    "# print len(data)\n",
    "plt.figure()\n",
    "plt.clf()\n",
    "plt.plot(np.arange(nS),data)\n",
    "plt.show()\n",
    "#------------------------------------------------------------------------------\n",
    "# CONV_LSTM\n",
    "# data = np.load('conv_lstm_AE_train_loss.npy')\n",
    "# #\n",
    "# # print np.shape(data)\n",
    "# print data[9834]\n",
    "# print np.min(data[0:12200])\n",
    "# # a = data[0:12200]\n",
    "# plt.figure()\n",
    "# plt.clf()\n",
    "# plt.plot(np.arange(12200),data[0:12200])\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
